Abstract

The asymptotical mean-square stability analysis problem is
considered for a class of Cohen-Grossberg neural networks (CGNNs) with random delay. The evolution of the delay is modeled by a continuous-time homogeneous Markov
process with a finite number of states. The main purpose of this paper is to establish
easily verifiable conditions under which the random delayed Cohen-Grossberg neural
network is asymptotical mean-square stability. By employing Lyapunov-Krasovskii
functionals and conducting stochastic analysis, a linear matrix inequality (LMI) approach is developed to derive the criteria for the asymptotical mean-square stability,
which can be readily checked by using some standard numerical packages such as the
Matlab LMI Toolbox. A numerical example is exploited to show the usefulness of the
derived LMI-based stability conditions.